import time
import matplotlib.pyplot as plt
from torch import nn
import torchvision.models as models
import numpy as np
import os

savepath = '../data/featureMap'
if not os.path.exists(savepath):
    os.mkdir(savepath)

def draw_features(width,height,x,savename):
    tic=time.time()
    fig = plt.figure(figsize=(16, 16))
    fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05)
    for i in range(width*height):
        plt.subplot(height,width, i + 1)
        plt.axis('off')
        # plt.tight_layout()
        img = x[0, i, :, :]
        pmin = np.min(img)
        pmax = np.max(img)
        img = (img - pmin) / (pmax - pmin + 0.000001)
        plt.imshow(img, cmap='gray')
        print("{}/{}".format(i,width*height))
    fig.savefig(savename, dpi=100)
    fig.clf()
    plt.close()
    print("time:{}".format(time.time()-tic))


class ft_net(nn.Module):

    def __init__(self):
        super(ft_net, self).__init__()
        model_ft = models.resnet50(pretrained=True)
        self.model = model_ft


    def forward(self, x):
        if True: # draw features or not
            x = self.model.conv1(x)
            draw_features(8,8,x.cpu().numpy(),"{}/f1_conv1.png".format(savepath))

            x = self.model.bn1(x)
            draw_features(8, 8, x.cpu().numpy(),"{}/f2_bn1.png".format(savepath))

            x = self.model.relu(x)
            draw_features(8, 8, x.cpu().numpy(), "{}/f3_relu.png".format(savepath))

            x = self.model.maxpool(x)
            draw_features(8, 8, x.cpu().numpy(), "{}/f4_maxpool.png".format(savepath))

            x = self.model.layer1(x)
            draw_features(16, 16, x.cpu().numpy(), "{}/f5_layer1.png".format(savepath))

            x = self.model.layer2(x)
            draw_features(16, 32, x.cpu().numpy(), "{}/f6_layer2.png".format(savepath))

            x = self.model.layer3(x)
            draw_features(32, 32, x.cpu().numpy(), "{}/f7_layer3.png".format(savepath))

            x = self.model.layer4(x)
            draw_features(32, 32, x.cpu().numpy()[:, 0:1024, :, :], "{}/f8_layer4_1.png".format(savepath))
            draw_features(32, 32, x.cpu().numpy()[:, 1024:2048, :, :], "{}/f8_layer4_2.png".format(savepath))

            x = self.model.avgpool(x)
            plt.plot(np.linspace(1, 2048, 2048), x.cpu().numpy()[0, :, 0, 0])
            plt.savefig("{}/f9_avgpool.png".format(savepath))
            plt.clf()
            plt.close()

            x = x.view(x.size(0), -1)
            x = self.model.fc(x)
            plt.plot(np.linspace(1, 1000, 1000), x.cpu().numpy()[0, :])
            plt.savefig("{}/f10_fc.png".format(savepath))
            plt.clf()
            plt.close()
        else :
            x = self.model.conv1(x)
            x = self.model.bn1(x)
            x = self.model.relu(x)
            x = self.model.maxpool(x)
            x = self.model.layer1(x)
            x = self.model.layer2(x)
            x = self.model.layer3(x)
            x = self.model.layer4(x)
            x = self.model.avgpool(x)
            x = x.view(x.size(0), -1)
            x = self.model.fc(x)
        return x